Avoidance and fear day by day in social anxiety disorder

被引:0
作者
Rodebaugh, Thomas L. [1 ,2 ,5 ]
Grossman, Jason T. [1 ]
Tonge, Natasha A. [3 ]
Shin, Jin [1 ]
Frumkin, Madelyn R. [1 ]
Rodriguez, Chavez R. [1 ]
Ortiz, Esteban G. [1 ]
Piccirillo, Marilyn L. [4 ]
机构
[1] Washington Univ St Louis, Dept Psychol & Brain Sci, St Louis, MO USA
[2] Univ North Carolina Chapel Hill, Dept Psychol & Neurosci, Chapel Hill, NC USA
[3] George Mason Univ, Dept Psychol, Fairfax, VA USA
[4] Univ Washington, Dept Psychol, Seattle, WA USA
[5] Univ North Carolina Chapel Hill, Dept Psychol & Neurosci, Campus Box 3270,235 E Cameron Ave, Chapel Hill, NC 27599 USA
关键词
social anxiety disorder; avoidance; idiographic models; exposure; cognitive behavioral models; LEARNING-THEORY; DEPRESSION; NECESSITY; EMOTION;
D O I
10.1080/10503307.2023.2297994
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
ObjectiveTheories assert that avoidance maintains maladaptive anxiety over time, yet a clear prospective test of this effect in the day-by-day lives of people with social anxiety disorder (SAD) is lacking.MethodWe used intensive longitudinal data to test prospective relationships between social fear and social avoidance in 32 participants with SAD who reported on a total of 4256 time points.ResultsResults suggested that avoidance strongly predicted future anxiety, but only in a minority of people with SAD. Relationships between anxiety and avoidance varied considerably across individuals. Pre-registered tests found that the strength of autocorrelation for social fear is a good target for future testing of prediction of exposure response. Participants with lower autocorrelations were less likely to show between-session habituation.ConclusionsOverall, results suggest avoidance maintains fear in SAD for at least some individuals, but also indicates considerable variability. Further intensive longitudinal data is needed to examine individuals with SAD across varying time courses.
引用
收藏
页码:282 / 295
页数:14
相关论文
共 45 条
  • [1] [Anonymous], 2016, Structured clinical interview for DSM-5 clinical version (SCID-5-PD)
  • [2] Asher M, 2021, BEHAV THER, V52, P183, DOI 10.1016/j.beth.2020.04.001
  • [3] Asparouhov T., 2022, Practical Aspects of Dynamic Structural Equation Models
  • [4] Dynamic Structural Equation Models
    Asparouhov, Tihomir
    Hamaker, Ellen L.
    Muthen, Bengt
    [J]. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2018, 25 (03) : 359 - 388
  • [5] Beck AT, 1996, PSYCHOL ASSESSMENT
  • [6] Are emotions frightening? II: an analogue study of fear of emotion, interpersonal conflict, and panic onset
    Berg, CZ
    Shapiro, N
    Chambless, DL
    Ahrens, AH
    [J]. BEHAVIOUR RESEARCH AND THERAPY, 1998, 36 (01) : 3 - 15
  • [7] Origins and outlook of interoceptive exposure
    Boettcher, Hannah
    Brake, C. Alex
    Barlow, David H.
    [J]. JOURNAL OF BEHAVIOR THERAPY AND EXPERIMENTAL PSYCHIATRY, 2016, 53 : 41 - 51
  • [8] Modeling Nonstationary Emotion Dynamics in Dyads Using a Semiparametric Time-Varying Vector Autoregressive Model
    Bringmann, Laura
    Ferrer, Emilio
    Hamaker, Ellen
    Borsboom, Denny
    Tuerlinckx, Francis
    [J]. MULTIVARIATE BEHAVIORAL RESEARCH, 2015, 50 (06) : 730 - 731
  • [9] Changing Dynamics: Time-Varying Autoregressive Models Using Generalized Additive Modeling
    Bringmann, Laura F.
    Hamaker, Ellen L.
    Vigo, Daniel E.
    Aubert, Andre
    Borsboom, Denny
    Tuerlinckx, Francis
    [J]. PSYCHOLOGICAL METHODS, 2017, 22 (03) : 409 - 425
  • [10] Assessing Temporal Emotion Dynamics Using Networks
    Bringmann, Laura F.
    Pe, Madeline L.
    Vissers, Nathalie
    Ceulemans, Eva
    Borsboom, Denny
    Vanpaemel, Wolf
    Tuerlinckx, Francis
    Kuppens, Peter
    [J]. ASSESSMENT, 2016, 23 (04) : 425 - 435